BigQuery, Looker and Petabyte-Scale Analytics in Google Cloud

As big data and data warehousing scale-up and move into the cloud, they’re increasingly likely to be delivered as services using distributed cloud query engines such as Google BigQuery, loaded using streaming data pipelines and queried using BI tools such as Looker. In this session the presenter will walk through how data modelling and query processing works when storing petabytes of customer event-level activity in a distributed data store and query engine like BigQuery, how data ingestion and processing works in an always-on streaming data pipeline, how additional services such as Google Natural Language API can be used to classify for sentiment and extract entity nouns from incoming unstructured data, and how BI tools such as Looker and Google Data Studio bring data discovery and business metadata layers to cloud big data analytics

Mark RittmanIndependent Analyst, rittman.co.uk

Mark is an ex-company founder and CTO now working as an independent product analyst in the big data, analytics and data integration space. He has around 20 years of hands-on implementation experience working with RDBMS DW, ETL and BI technologies and more recently, cloud-based big data analytic platforms from Google, Amazon and Oracle and is currently working with startups and other product organizations in Europe and the US helping them take their big data analytics products to market. Mark runs a blog on Medium (https://medium.com/mark-rittman) and hosts the Drill to Detail Podcast and website (https://www.drilltodetail.com), and has links to past presentations, white papers and published articles on his personal website (http://rittman.co.uk/)